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Abdelrhman, F.

Publications and source records attributed to Abdelrhman, F..

2 recordsLinked to original sources

Epitope-Based Peptide Vaccine Design Against Fructose Bisphosphate Aldolase of Candida Glabrata: An Immunomics Approach

Background Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although, there are multiple effective vaccines against fungal infections, and some of these vaccines were engaged in different stages of clinical trials, none of them yet approved by (FDA).Aim To predict the most conserved and immunogenic B- and T-cell epitopes from the Fructose Bisphosphate aldolase (Fba1) protein of C. glabrata.Materials and Methods 13 C. glabrata Fructose bisphosphate aldolase protein sequences (361amino acid) were retrieved from NCBI and several in silico tools presented in the IEDB server for predicting peptides were used and homology modeling and molecular docking were performed.Result The promising B-cell Epitopes were AYFKPH, VDKESLYTK, and HVDKESLYTK. While, promising peptides which have the high affinity to MHC I binding were: AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL and YFKEHGEPL. Two peptides (LFSSHMLDL and YIRSIAPAY) were noted to have the highest affinity to MHC class II that interact with 9 MHC class II alleles. The molecular Docking revealed the epitopes QTSNGGAAY and LFSSHMLDL have the high binding energy to MHC moleculesConclusion The epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines that they are more specific, less time consuming, safe, less allergic and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidates epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cells epitopes from Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. galabrata.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

Novel deleterious nsSNPs within MEFV gene that could be used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using bioinformatics analysis

BackgroundFamilial Mediterranean Fever (FMF) is the most common auto inflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin, we aimed to identify the pathogenic SNPs in MEFV by computational analysis software.\n\nMethodsWe carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function.\n\nResult23 novel mutations out of 857 nsSNPs that are found to be deleterious effect on the MEFV structure and function.\n\nConclusionThis is the first in silico analysis in MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.

bioinformatics↗